Novelty Detection in Underwater Acoustic Environments Using Out-of-Distribution Detector for Neural Networks 


Vol. 50,  No. 12, pp. 1839-1841, Dec.  2025
10.7840/kics.2025.50.12.1839


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  Abstract

In this paper, we propose an ODIN-based novelty detection framework to effectively identify unknownacoustic signals in underwater environments. Specifically, temperature scaling and input perturbation are applied to the softmax output of a pre-trained classifier to induce differences between known and unknown samples, and the calibrated maximum softmax probability is used as a novelty score to perform novelty detection.

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[IEEE Style]

N. Kim, C. Chun, H. K. Kim, "Novelty Detection in Underwater Acoustic Environments Using Out-of-Distribution Detector for Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 12, pp. 1839-1841, 2025. DOI: 10.7840/kics.2025.50.12.1839.

[ACM Style]

Nayeon Kim, Chanjun Chun, and Hong Kook Kim. 2025. Novelty Detection in Underwater Acoustic Environments Using Out-of-Distribution Detector for Neural Networks. The Journal of Korean Institute of Communications and Information Sciences, 50, 12, (2025), 1839-1841. DOI: 10.7840/kics.2025.50.12.1839.

[KICS Style]

Nayeon Kim, Chanjun Chun, Hong Kook Kim, "Novelty Detection in Underwater Acoustic Environments Using Out-of-Distribution Detector for Neural Networks," The Journal of Korean Institute of Communications and Information Sciences, vol. 50, no. 12, pp. 1839-1841, 12. 2025. (https://doi.org/10.7840/kics.2025.50.12.1839)
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pISSN : 1226 - 4717
eISSN : 2287 - 3880